Forecasting Model Selection with Variables Impact to Predict Electricity Demand at Rajshahi City of Bangladesh
Keywords:Forecasting, Soft Computing, Fuzzy Linear Regression, Root Mean Square Error, Correlation Coefficient, Forecasting Error
The purpose of this study is to forecast electricity demand by using the best-selected method which untangles all the factors that affect electricity demand. Three different methods traditional methods (Multiple Regression Model), modified-traditional methods (ARMA), and soft computing method (Fuzzy Linear Regression Model) are selected for prediction. Environmental parameters like temperature, humidity, and wind speed are included as variables as Rajshahi has very impactful weather. The impact of each variable was calculated from their standardized values to know the effect of environmental parameters. The accuracy of the three forecasting models is compared by different statistical measures of errors. Using Mean Absolute Percentage Error (MAPE), the errors of the Multiple Regression Model, ARMA, and Fuzzy Linear Regression (FLR) Model are 6.85%, 22.24%, and 4.45%. The other three measures of error also give the FLR gives the best results. Finally, the electricity demand of Rajshahi City for the next five years is forecasted using the Fuzzy Linear Regression Model.
Wang, W.C., Chau, K.W., Cheng, C.T. and Qiu, L., 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374(3-4), pp.294-306. DOI: https://doi.org/10.1016/j.jhydrol.2009.06.019
Wang, S. and Chaovalitwongse, W.A., 2011. Evaluating and comparing forecasting models. International Journal of Forecasting, 14(1), pp.35-62. DOI: https://doi.org/10.1002/9780470400531.eorms0307
Singh, A.K., Khatoon, S., Muazzam, M. and Chaturvedi, D.K., 2012, December. Load forecasting techniques and methodologies: A review. In 2012 2nd International Conference on Power, Control and Embedded Systems (pp. 1-10). IEEE. DOI: https://doi.org/10.1109/ICPCES.2012.6508132
Azadeh, A., Saberi, M. and Gitiforouz, A., 2013. An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption. Quality & quantity, 47, pp.2163-2176. DOI: https://doi.org/10.1007/s11135-011-9649-0
Monne, M.A. and Alam, K.S., 2013. Application of fuzzy logic to electric load forecasting (ELF). Int. Journal of Science and Advanced Technology, 3, pp.47-50.
Islam, A., Hasib, S.R. and Islam, M.S., 2013. Short term electricity demand forecasting for an isolated area using two different approaches. Journal of Power Technologies, 93(4), pp.185-193.
Kaytez, F., Taplamacioglu, M.C., Cam, E. and Hardalac, F., 2015. Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, pp.431-438. DOI: https://doi.org/10.1016/j.ijepes.2014.12.036
Sarkar, M.R., Rabbani, M.G., Khan, A.R. and Hossain, M.M., 2015, May. Electricity demand forecasting of Rajshahi City in Bangladesh using fuzzy linear regression model. In 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT) (pp. 1-3). IEEE. DOI: https://doi.org/10.1109/ICEEICT.2015.7307424
Xie, J. and Hong, T., 2017. Wind speed for load forecasting models. Sustainability, 9(5), p.795. DOI: https://doi.org/10.3390/su9050795
P. Ganguly, A. Kalam, and A. Zayegh, “Short term load forecasting,” in International Conference on Research in Education and Science (ICRES), 2017, pp.355–361.
Anand, A. and Suganthi, L., 2020. Forecasting of electricity demand by hybrid ANN-PSO models. In Deep learning and neural networks: Concepts, methodologies, tools, and applications (pp. 865-882). IGI Global. DOI: https://doi.org/10.4018/978-1-7998-0414-7.ch048
Nagbe, K., Cugliari, J. and Jacques, J., 2018. Short-term electricity demand forecasting using a functional state space model. Energies, 11(5), p.1120. DOI: https://doi.org/10.3390/en11051120
Eshragh, A., Ganim, B., Perkins, T. and Bandara, K., 2022. The importance of environmental factors in forecasting australian power demand. Environmental Modeling & Assessment, 27(1), pp.1-11. DOI: https://doi.org/10.1007/s10666-021-09806-1
Bedi, J. and Toshniwal, D., 2019. Deep learning framework to forecast electricity demand. Applied energy, 238, pp.1312-1326. DOI: https://doi.org/10.1016/j.apenergy.2019.01.113
Shah, I., Jan, F. and Ali, S., 2022. Functional data approach for short-term electricity demand forecasting. Mathematical Problems in Engineering, 2022. DOI: https://doi.org/10.1155/2022/6709779
Rajshahi Population 2023, Available at: https://worldpopulationreview.com/world-cities/rajshahi-population. Date of Access: 10th April 2023.
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Copyright (c) 2023 Md Rasel Sarkar, Lafifa Margia Orpa, Rifat Afroz Orpe
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